Better homes, better health: insights from linking housing and health data

Decent housing is an essential building block of health. But for many households in the UK, a safe, warm and affordable home remains out of reach. Our previous work has highlighted the urgent need for housing reform to improve health and proposed a policy roadmap to move the UK towards healthy homes. Many of these proposals, such as stricter requirements for landlords to fix housing hazards and an updated Decent Homes Standard, have since become government policy.

These policy advances are a step in the right direction, but their success will depend on adequate implementation. The levers of delivery and enforcement are largely in the hands of local decision makers, at a time when local authorities are overburdened and underfunded. Policy effects will also take time to manifest – for many households, support is needed right now.  

Local authorities need reliable evidence to ensure limited resources are being spent effectively and in the places with the greatest need. For instance, if a housing intervention is rolled out to improve local population health, how can we evaluate its success or if it’s reaching the highest-need households? How can we identify what support is needed, and which households or population groups have greater support needs? 

Our data infrastructures lag behind what’s needed to answer these questions. Patient-level health data linked to property-level housing data can play a key role by supporting local decision makers to develop a comprehensive understanding of population health needs and the impact of interventions. However, these data linkages are not widely accessible.

About this briefing

In five areas across England, Scotland and Wales, the Networked Data Lab (NDL) established novel linkages between data on housing and health to fill these evidence gaps for local stakeholders. In this briefing, we illustrate the central role linked data on housing and health has to play in both deepening our understanding of the relationship between housing and health and providing local decision makers with the evidence needed for improvements. 

We also share insights from the NDL teams’ local analyses of key questions on housing and health in their local areas, covering Cheshire and Merseyside, Grampian, North West London, Wales and West Yorkshire. We also explore:

• why linking data on housing and health is challenging
• what linked data tell us about housing and health
• how linked data can inform housing interventions
• how we can better use data on housing and health

For more information on the local analyses performed by the NDL teams, the range of data they used and the methods they employed, please see Appendix 1 and the teams’ final reports on GitHub.

As a part of this project, the Health Foundation and the NDL teams established and engaged several panels of people with direct experience of health challenges associated with damp and mould and trouble heating a home. The panel’s insights helped inform our analysis and interpretation of the findings presented. 

Disentangling the relationship between housing and health is notoriously difficult. Housing conditions interact with a web of wider determinants of health such as access to education, transport and healthy food, and their effects often accumulate and emerge over long time periods. 

Much of the existing evidence base on housing and health relies on survey data, typically using self-reported health metrics rather than health care use totals from administrative data. Where administrative health records are used in research, housing information is often only available at an area level, such as by local authority. This limits the ability to isolate the specific effects of housing conditions from other factors, such as deprivation level. As a result, it remains difficult to precisely identify need, effectively target interventions and coordinate support across housing and health systems. 

The complex nature of housing’s effect on health requires a linked data approach for effective analysis, where data about people’s health are combined with data on their housing situation at an individual level. Linked data take a comprehensive view of individuals’ interactions with the health care system over time and place them in the context of wider demographic and socioeconomic characteristics such as age, sex, ethnicity or deprivation level. This multidimensional, system-wide view from linking property-level housing data with patient health records is needed to enable population-level analysis of how poor housing affects health and support household-level interventions. 

Data linkage is equally important from a systems operation perspective, allowing different aspects of the health and care system to share information and act in concert – a key enabler of the integrated, community-based approach to care set out in the NHS 10 Year Plan for Health. NHS England’s 2025/26 neighbourhood health guidelines recognise person-level linked datasets as a core component of effective community health care, further recommending that localities should incorporate housing data into their routinely collected population health datasets.  

Recent years have seen promising developments. Linkage between patient identifiers in NHS hospital data and the Unique Property Reference Numbers (UPRNs) of patients’ residences has been established in some integrated care boards (ICBs) in England, with plans for this to be available to all ICBs in the near future. 

Similarly, linkage between UPRNs and Community Health Index numbers, the unique patient identifiers used by NHS Scotland, has been facilitated across Scotland since 2020. In Wales, UPRNs have been linkable to health data in the SAIL Databank, a trusted research environment, through a Residential Anonymous Linking Field for over a decade. 

Despite these advances, in most localities, data on housing and health has still not been linked for research purposes or operational work such as housing planning, targeting interventions or evaluating improvement programmes. This not only limits our understanding of housing’s effect on health but leaves gaps in local health intelligence. 

Through engaging with stakeholders in their local authorities and health boards, the NDL teams found that decision makers had many questions that cannot be answered using the data they currently hold. These questions revolved around how well housing improvement programmes are targeted, the populations afflicted by certain housing hazards and the health profiles of vulnerable household types (see Table 1).

For more information on the NDL teams’ local analyses, including details on stakeholder engagement, data sources and methodologies, please see Appendix 1. 

New insight into housing-related health inequalities

By linking housing data with health, demographic and socioeconomic information, we can build a more detailed picture of how housing contributes to health inequalities. This approach enables analysis of how exposure to housing hazards varies across different groups, including age, deprivation level, ethnicity and first language. 

NDL North West London found that black or black British individuals are more than twice as likely to have a record of damp and mould exposure in their primary care records compared with white individuals, after controlling for wider demographic and socioeconomic factors (Figure 1). Black or black British people are also more likely to also be exposed to fuel poverty, making up almost one-quarter of those living with both damp and mould and fuel poverty despite accounting for less than one-tenth of the North West London population. Damp and mould exposure also varies between speakers of different languages, with Arabic and Urdu speakers having a higher likelihood of exposure compared with English first-language speakers. 

These factors all have tangible impacts on health. Children who live in a home with recorded damp and mould are significantly more likely to have respiratory and mental health conditions compared with children not exposed to damp and mould but with otherwise similar demographic and socioeconomic characteristics. People who live with children in such homes are more than twice as likely to have had a mental health observation compared with those living in other households. People aged 65 years and older who have been exposed to fuel poverty are also significantly more likely to have diagnoses of respiratory, mental health and chronic pain conditions compared with similar households.

NDL labs in Cheshire and Merseyside, Wales and West Yorkshire all used systems of linked data to explore how the delivery of local housing interventions aligned with local population need and the programmes’ targeting intentions. 

Targeting and prioritising housing interventions

NDL Cheshire and Merseyside used linked data to evaluate the distribution of the Liverpool City Region’s retrofit programme, which assists households with improving their energy efficiency. They found that high health care need areas such as Liverpool, Knowsley and St Helens had lower relative retrofit coverage despite having the highest concentrations of clinically vulnerable households. In total, around 1 in 12 households in the Liverpool City Region had high requirements for retrofit based on demographic, clinical and environmental factors but were located in areas where retrofit assistance was scarce. 

Given that improving health and reducing inequalities are goals of the retrofit programme, using linked health data would have added far greater specificity to its targeting. The index designed by NDL Cheshire and Merseyside for assessing local health need for retrofit has since been expanded and made available for the local authority to target housing improvements.  

These findings demonstrate how health data should play a key role in identifying households or areas for assistance from local programmes, ensuring that investment and resources reach where the health risks associated with cold, damp and inefficient homes are most acute. 

Evaluating if a programme is reaching those most in need

NDL West Yorkshire examined a pilot of selective licensing in Leeds. Selective licensing requires landlords to obtain a licence to privately rent out properties in areas experiencing problems related to housing quality and deprivation. The NDL team observed that areas chosen for selective licensing had much higher rates of conditions associated with poor-quality housing, such as chronic obstructive pulmonary disease, compared with other areas in Leeds, as well as higher rates of long-term health conditions. On this basis, the NDL team was able to identify other areas with similar health profiles that may benefit from the policy. 

NDL Wales similarly explored the characteristics of beneficiaries of Warm Wales, a fuel poverty alleviation programme offering support to economically disadvantaged households. Warm Wales recipients were found to be concentrated in the most deprived areas of Wales and have nearly double the emergency admission rate and a higher multimorbidity rate compared with the general Welsh population. This signals that the programme is reaching its intended recipients and providing assistance where most needed.

Streamlining local data-sharing practices

In all NDL locations, information governance processes for accessing required data were lengthy and difficult. In most cases, data sharing provisions have not yet been built to routinely incorporate data on housing into local health data flows. Data are also often held by several different providers. This lengthy lead-in time meant that some more complex analyses were not possible within the project’s timeframe, such as long-term evaluations of the impact of local housing interventions on population health. 

The NDL teams all have extensive existing data linkage capabilities and were chosen for this project on that basis. Under current conditions, analysts without such experience or resources would likely find linkage between housing and health data impossible. As the linkage of property-level data with health data becomes possible across the country, these difficulties with information governance processes could considerably slow progress incorporating them into local population health datasets. 

To make housing data useable for health intelligence and evaluation, local authorities, ICBs and central government must streamline information governance processes. This can be facilitated by:

• creating multi-agency data-sharing agreements to ease data flows and promote understanding between data providers
• producing blanket Data Protection Impact Assessments that can cover multiple projects and analytical uses
• instituting strategic alignment and close collaboration between information governance, data management and analytical teams
• having clearer guidance and processes for incorporating centrally held data on housing, such as UPRNs, into local analyses. 

These practices can smooth the process of establishing stable local linkages between patient-level datasets on health and property-level datasets on housing for health intelligence and programme evaluation, while avoiding a lengthy information governance process being necessary for every use of the data. 

Improving housing in the UK is essential for improving population health and remedying health inequalities. Given the government’s ambition to shift the health system towards prevention, housing should be as central a concern to health decision makers as diet or smoking – just as the health of residents should be a central concern of housing planners. 

Delivering the ambitions of recent housing policy will be largely the responsibility of time- and resource-poor local decision makers. Success depends on knowing whether spending is going to the places with the greatest need and having the greatest possible impact. Failure to effectively target housing improvement resources has a very real and immediate price, with poor-quality housing directly costing the NHS an estimated £1.4bn per year.

Our work shows that linking data on housing and health empowers local authorities to direct resources towards the interventions that will have the greatest effect and the population groups with the greatest need. Housing and health intelligence has the further potential to unlock increased funding for housing interventions by demonstrating their ability to improve people’s health and their cost effectiveness from a health perspective. 

We make the following recommendations to help realise the benefits of linking data on housing and health.

Enable secure, routine data sharing between housing and health datasets

• Local authorities should be given access to relevant health insights on their populations to support the prioritisation and evaluation of housing improvements. Health care providers and commissioners should similarly be given access to relevant information on housing to support decisions in health and care.
• National and local bodies should streamline data access processes and build on existing data-sharing agreements to enable wider linkage of data on housing and health across the UK.

Provide national leadership on data linkage and governance

• Central government should set clear expectations for and provide practical guidance on information governance for linking data on housing and health, building on existing frameworks such as NHS England’s neighbourhood health guidelines.
• National bodies should actively support local areas to develop linked datasets and build analytical capacity to unlock those data’s value, reducing duplication of effort and accelerating progress.

Support operational use of linked data

• Linked data on housing and health should be usable within secure, operational settings in the NHS and local authorities to inform day-to-day decision making.

Strengthen housing data infrastructure

• The Private Rented Sector Database introduced through the Renters’ Rights Act should be integrated into wider data systems as these data become available, helping to address current gaps in understanding of the health impacts of the private rented sector.

Use data to proactively target interventions

• Local areas should safely use linked data to identify and prioritise households most in need of housing improvements, enabling more effective targeting of programmes such as retrofitting.
• Where household-level targeting is not yet feasible, local areas should use the most granular geographic data available (for example, at postcode or street level).

Build public trust and legitimacy

• Local and national organisations should engage the public on how data on housing and health are used, ensuring transparency, strong safeguards and clear public benefit to build confidence in data sharing.

The NDL teams’ local analyses show the practical value of linking data on housing and health to inform better decisions around intervention funding and targeting. But they are only a first step. The possible benefits of linked data on housing and health are immense – the long-term, system-wide view they enable allows for far more precise identification of the impacts of poor housing and the success of interventions. Yet without more systematic data sharing and stronger national and local leadership, opportunities to improve health and reduce inequalities will continue to be missed. Embedding linked data into routine decision making is essential if housing investment is to be targeted effectively, deliver measurable health benefits and make the most of constrained public resources.